There are various methods of determining a position of specific coordinates on three-dimensions. As an example of the representative method, there is a radar tracking recognition system that may determine coordinates of one point on three-dimensions using an azimuth, elevation, and a distance.
Meanwhile, a global positioning system (hereinafter, referred to as “GPS”) that has been originally developed for military purposes has been used in a variety of fields as military or civilian purposes, as the use of GPS satellites has been also opened to civilians. The GPS has been utilized in a wide variety of fields such as automatic navigation systems for vehicles, ships, aircraft, or the like, various services (for example: NATE or magicN) used to determine positions of children, the elderly, friends, or the like provided by mobile carriers, strike of advanced guided weapons (Tomahawk missiles), movement of ground troops, and the like. Here, in order to determine accurate position information, GPS signals should be respectively received from mutually different satellites (at least three satellites).
However, the GPS signals have weak signal strength, and are hampered by special circumstances such as ionosphere of the atmosphere until the GPS signals are transmitted to a GPS receiver on the Earth's surface from the satellites in the universe, tunnels, overpasses, and the like. That is, absolute coordinates (three-dimensional (3D) position coordinates and the time) provided by the GPS signals are highly likely to have an error.
Thus, methods for correcting the error have been developed.
In a case of location based service (LBS), the absolute coordinates may be transmitted from the GPS satellites and the error of the position information may be corrected by searching multiple paths using data stored within a server. In a case of differential-GPS (D-GPS), a standard GPS receiver may be installed in a specific place, and the error of the position information of a moving GPS receiver may be corrected by comparing an accurate position of the standard GPS receiver and data of the moving GPS receiver.
However, when the moving GPS receiver (in practice, a mobile terminal equipped with the moving GPS receiver) is moved to an environment in which the GPS signals cannot be received such as in an underground parking lot, the inside of a building, an overpass, a tunnel, or the like or the GPS signals can be received from only three satellites or less, there is a problem that cannot obtain current position information.
In addition, when using only the absolute coordinates provided by the GPS signals, an error range reaches several tens of meters to several hundred meters, and therefore there is a problem that cannot provide accurate position information.
In particular, in recent years, an indoor navigation field that estimates a pedestrian position indoors has attracted attention.
The GPS signals are hardly received by the walls of the building in such an indoor environment, and therefore the GPS technology cannot be used in the indoor navigation field.
Thus, in order to solve this problem, a pedestrian position estimation method using a micro electro mechanical system (MEMS) sensor module and a pedestrian position estimation method using a mobile device have been conventionally proposed.
First, the pedestrian position estimation method using the MEMS sensor module is basically based on a pedestrian dead-reckoning (PDR) technique, that is, a technique that continuously estimates a position of a pedestrian by estimating the number of steps, a gait, and a direction of the pedestrian. The MEMS sensor module is fixed and attached to a part of the body such as head, legs, or waist of the pedestrian so that the axis of the sensor module is fixed, and therefore noise according to the motion of the pedestrian is small and the direction is fixed, resulting in facilitating to perform the PDR. However, the pedestrian position estimation method using the MEMS sensor module has a quite inconvenient drawback because the sensor module should be attached to the body and signals from the sensor module should be received and processed through specific equipment.
Meanwhile, with the launch of the latest high-performance mobile devices, the hardware performance has been improved and a variety of sensors have been built in the mobile devices. A method in which this trend is reflected is the pedestrian position estimation method using the mobile device.
However, in the pedestrian position estimation method using the mobile device, when a pedestrian moves while carrying the mobile device, the axis of the mobile device is moved according to the motion of the pedestrian unlike the MEMS sensor module. In general, the pedestrian moves while holding the mobile device in one hand or putting in a pocket, and therefore a lot of unnecessary noise occurs. In addition, the sensors built in the mobile device have lower performance than that of the MEMS sensor.
Due to these problems, only through the PDR technique using the existing MEMS sensor module, there are difficulties in estimating a pedestrian position using the mobile device.